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Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Pearl millet diseases like rust and blast significantly reduce crop yield and quality.
  • Traditional disease detection methods are labor-intensive, costly, and require expert intervention.
  • Automated solutions are needed to assist farmers in timely disease identification and management.

Purpose of the Study:

  • To develop an integrated IoT and deep learning framework for automatic data collection and classification of pearl millet diseases.
  • To design and deploy a novel deep learning model ('Custom-Net') for precise disease detection.
  • To evaluate the effectiveness of transfer learning and feature visualization techniques in disease classification.

Main Methods:

  • An 'Automatic and Intelligent Data Collector and Classifier' framework was developed, integrating IoT devices and deep learning.
  • Imagery and parametric data were collected from pearl millet fields and sent to cloud and Raspberry Pi systems.
  • A 'Custom-Net' model was designed and deployed, utilizing Grad-CAM for feature visualization and transfer learning.
  • Performance was compared against state-of-the-art models including Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19.

Main Results:

  • The 'Custom-Net' model achieved a classification accuracy of 98.78%, comparable to state-of-the-art models.
  • Transfer learning was shown to improve feature extraction by the 'Custom-Net' model.
  • The proposed model significantly reduced training time by 86.67% compared to other models.
  • Grad-CAM visualization confirmed that 'Custom-Net' effectively extracts relevant disease-related features.

Conclusions:

  • The developed framework provides a low-cost, automated solution for detecting pearl millet diseases.
  • The 'Custom-Net' deep learning model offers high accuracy and efficiency in disease classification.
  • The integration of IoT and deep learning facilitates timely disease management, potentially improving crop yield and quality.